Introduction
Chronic inflammatory rheumatism refers to the inflammation of joints, muscles and surrounding soft tissues with joint pain as the main manifestation. Among them, RA and SpA are the most common diseases that irreversibly damage joints, which seriously affect the life quality of patients [
1]. However, due to the similar clinical manifestations and the lack of feasible biomarkers, this kind of disease is difficult to identify and diagnose. Current studies have found that immune factors play a vital role in the whole process, especially in the early stage [
2,
3]. Therefore, finding new biomarkers and revealing immune mechanism are the key to early prevention and treatment.
The basic pathological changes of RA are chronic inflammation of synovium, pannus formation, and gradual destruction of articular cartilage and bone, resulting in joint deformity and loss of function [
4]. At present, it is considered that HLA-DRB1 allele mutation is related to the disease [
5]. In addition, abnormal immune regulation is also an important factor in the occurrence and development of RA. A large number of studies have shown that immune cells will infiltrate the joint synovium, such as activated CD4+ T cells, start a specific immune response and lead to the corresponding symptoms of arthritis [
6]. However, CD8+ T cells have anti-inflammatory properties and may help to reduce the persistent autoimmune response of rheumatoid joints [
7]. In addition, macrophages can secrete a large number of cytokines, chemokines and degrading enzymes, leading to joint inflammation and bone destruction [
8]. Therefore, the study of immune cells in synovium is very important for the treatment of RA.
SpA, also known as serum negative spondyloarthritis, is a general term of chronic inflammatory rheumatism with the main manifestations of involving the spine and peripheral joints, or ligaments and tendons [
9]. The disease has familial aggregation, but people with HLA-B27 gene do not necessarily suffer from the disease [
10]. The etiology of SpA is not clear. Studies have shown that cytokines such as tumor necrosis factor α(TNF-α) and IL-17 can mediate the imbalance of immune and stromal cells, leading to bone remodeling [
11]. However, the early diagnosis of SpA is difficult, and the research on immune cells is still limited [
12]. So far, there is no study using CIBERSORT to analyze immune cells infiltration of patients with SpA.
In this study, we obtained DEGs between RA, SpA patients and normal controls. We not only analyzed its function enrichment, but also analyzed the relationship between immune cells. Most importantly, we also analyzed the differences of immune cells between the two diseases and screened diagnostic markers. This provides a direction for in-depth understanding of chronic inflammatory rheumatism and guiding diagnosis and treatment.
Materials and methods
Datesets download
We used “rheumatoid arthritis” or “spondyloarthritis” as keywords to search for element related datasets in GEO database (
https://www.ncbi.nlm.nih.gov/geo/). The inclusion criteria are as follows: (1)
Homo sapiens microarray analysis of RA and SpA with complete data; (2) Tissue samples were taken from the patient’s knee synovium; (3) Patients had no other immune diseases. Three eligible datasets were selected, GSE41038 was used to compare SpA and normal controls. GSE12021 included twelve RA patients and four normal controls for comparison. The comparison between RA and SpA used dataset GSE30023.
Identification of DEGs
The difference analysis was carried out by microarray data linear model (Limma) software package. The
p value less than 0.05 and |log2-fold change (FC)| > 1 were considered to be statistically significant. The results of DEGs are presented by volcano map. The PPI network of DEGs was predicted using online tool STRING (
https://string-db.org/). Network diagram uses Cytoscape software (v3.8.0) to achieve better visualization.
Screening of hub genes
Hub gene has more connections in PPI network and usually plays an important role in diseases. Cytohubba is a built-in tool in Cytoscape, which uses different methods to identify hub genes in the network. We calculate the top 20 hub genes by degree, maximum cluster centrality (MCC) and maximum neighborhood component (MNC). The software package “Heatmap” is used to visualize the up and down regulation of hub genes.
Functional enrichment analysis
We used two different methods for enrichment analysis to improve accuracy. Gene set enrichment analysis (GSEA) compared the differential expression of all genes in the two types of samples. However, the analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was only aimed at the differential genes. p < 0.05 is considered to be statistically significant. The enrichment pathways and functions were visualized by ggplot2 package.
Evaluation of immune cells
CIBERSORT can transform the standardized gene expression matrix into the composition of invasive immune cells. Upload data to CIBERSORT (
https://cibersort.stanford.edu/). The website defined 22 components of infiltrating immune cells using LM22 characteristic matrix, and only the data with
p value < 0.05 were retained. Violin diagrams are used for visualization. Psych package is used to calculate the correlation coefficients of various immune cells.
Principal component analysis (PCA)
We used GraphPad Prime 9 for PCA cluster analysis of gene matrix data. The intra group data repeatability of the dataset was tested by PCA cluster analysis. Using the same analysis of immune cell infiltration matrix data, two-dimensional PCA clustering results were obtained.
Predictive biomarkers and value analysis
Overlapping genes between datasets serve as potential diagnostic markers, as shown in the Venn diagram. Receiver operating characteristic (ROC) curves were performed by GraphPad Prime 9 to predict the diagnostic effectiveness of biomarkers. The area under ROC curve (AUC) was calculated. AUC > 0.8 showed that biomarkers had good diagnostic value.
Correlation analysis
The correlation of the identified diagnostic biomarkers with the levels of infiltrating immune cells was explored using Spearman’s rank analysis. Use the bubble chart and “ggplot2” package to visualize the results.
qRT‐PCR and statistical analysis
Based on the results of the above analysis, the synovium samples of 3 patients with RA and 3 patients with SpA were obtained from knee arthroscopy, and the synovium of 3 patients with meniscus injury was used as control to verify the expression levels of six diagnostic markers. Total RNA was extracted from synovial membrane using the TRIzol reagent (Beyotime, China) according to the manufacturer’s instruction, and reversely transcribed. QRT-PCR was performed on CFX connect real-time PCR detection system (Bio-rad, USA). The relative gene expressions were calculated by the 2−ΔΔCt method. GAPDH was selected to normalize the expression levels of the target genes. All experiments were performed independently in triplicate.
The sequences of specific primers are as follows: C19orf12 (5′-ATCGGTTACGGATCGAACA-3′), ALPK2 (5′‐GCGAAGACCTTGGCATTTATT‐3′), S1PR3 (5′-GTGATCCTCTACGCACGCATC-3′), MZB1 (5′-CTCACAGGCCCAGGACTTAG-3′), XIST (5′-CTCTCCATTGGGTTCAC-3′), CCDC88C (5′‐TCTGGTGACCTGGGTGAAAA‐3′) and GAPDH (5′‐CCGTTGAATTTGCCGTGA‐3′).
Discussion
RA and SpA are the most common chronic inflammatory diseases causing multi joint pain. Because of the continuous destruction of joints, early diagnosis and treatment are particularly important [
13]. However, the clinical manifestations of early diseases are similar, the diagnosis time is too long, and there is no effective biomarker, especially the negative serum rheumatoid factor and anti-cyclic citrulline antibody, which bring great difficulties to the treatment [
14]. Increasing number of studies have shown that the inflammatory microenvironment and inflammatory cells of synovium play an indispensable role in diseases [
15,
16]. Therefore, it is of great significance to study the infiltrating immune cells in synovial tissue and find new differential diagnostic markers.
Compared with the normal control group, the number of B cells memory, plasma cells, T cells CD4 naive and activated dendritic cells in RA synovium increased significantly, and the number of M2 macrophages decreased significantly. Previous studies have shown that B cells activate and differentiate into plasma cells, secrete a large amount of immunoglobulin and form a complex with rheumatoid factor, which can induce inflammation after complement activation [
17]. Special components in synovial tissue and endogenous substances produced in vivo can also be presented by dendritic cells as self-antigens, activate CD4+ T cells and lead to inflammation [
18]. T cells follicular helper (Tfh) is a subtype of CD4+ T cells, which can help B cells and regulate the production of antibodies, so as to further participate in the occurrence of RA [
19]. In addition, inducing anti-inflammatory M2 macrophages, inhibiting the production of inflammatory factors and alleviating synovitis of RA are also the focus of current research. This is consistent with our experimental results [
20]. Enrichment analysis also showed that cellular immune processes such as B cells differentiation, T cells proliferation, cell adhesion, cell chemotaxis and endocytosis were involved in the pathogenesis of RA. The above results show that B cells, T cells, dendritic cells and macrophages are the key cells in the occurrence and development of RA.
Many patients with SpA first show swelling and pain of peripheral joints, and then appear symptoms of low back pain several years later. The lack of specific laboratory test indicators has brought great difficulties to disease diagnosis [
21]. It can be seen from our experiment that SpA has more B cells memory, neutrophils and less activated dendritic cells than normal control. There are also differences between M2 macrophages and mast cells. Current studies have shown that dendritic cells overproduce cytokines and migrate to potential inflammatory sites, where both immune cells of the innate immune system and cells of the adaptive immune system are activated to produce more pro-inflammatory cytokines. These cytokines can in turn interact with receptors on effector cells, such as macrophages and neutrophils, leading to tissue destruction [
22,
23]. At the same time, we also found that SpA is highly similar to RA in the direction of functional enrichment such as cell chemotaxis and immunoglobulin production. More interestingly, the DEGs enrichment pathway of SpA is related to RA. The two diseases are difficult to distinguish, especially in the early stage of the disease. Although the results of immune cells infiltration showed that there was no significant difference between the two diseases, DEGs were enriched in B cells related functions. GSEA was also significantly enriched in B cells and T cells related immune regulation. Previous SpA studies have found that innate immune system activation seems to be more important than more typical adaptive immune system diseases such as RA [
24]. Our pathway analysis also showed that innate immunity, such as natural killer cell-mediated cytotoxicity and primary immunodeficiency, were significantly enriched. These results suggest that SpA may be related to innate immune cells such as NK cells, and the number of T cells, B cells is less than RA.
In order to further study the diagnostic markers of chronic rheumatoid arthritis, we finally screened the same three genes (C19orf12, ALPK2, S1PR3) in the DEGs between the two diseases and normal controls. ROC regression analysis found that the three genes had good specificity and sensitivity, but only two had high correlation with immune cells (C19orf12, S1PR3). Among them, C19orf12 plays an important role in the immune cell infiltration of SpA, and S1PR3 is closely related to the immune cell infiltration of RA. S1PR3 is a bioactive sphingolipid that regulates signaling pathways essential to biological processes, including cell growth, immune cell transport and inflammation [
25]. In our study, we found that the expression of S1PR3 in RA patients was increased, which was consistent with the experimental results of Takuya. Inhibiting S1PR3 can reduce the production of pro-inflammatory cytokines and bone destruction, so as to treat autoimmune arthritis [
26]. So far, the exact cellular function of C19orf12 and its relationship with immune diseases are not clear. This protein is commonly expressed, but especially in the brain, blood cells and adipocytes [
27]. Through the verification of qRT-PCR, we finally determined S1PR3 as a biomarker for the early diagnosis of immune arthritis.
In addition, four genes were screened from DEGs of SpA and RA, three of which were highly related to immune cells (MZB1, XIST, CCDC88C). These three genes have been confirmed to be related to the occurrence of RA [
28‐
30]. MZB1 plays an important role in humoral immune response and is related to a variety of immune cells. It can enhance the ability of B cells to differentiate into plasma cells, which is the same as the pathogenesis of RA [
31]. XIST leads to RA by inhibiting cell proliferation and inducing apoptosis, and is considered as a diagnostic marker [
32]. Neither of these two genes has been reported to be associated with SpA and has high diagnostic value, so we use them as markers to distinguish RA from SpA. In addition, these three genes are related to T cells follicular helper, T cells gamma delta, NK cell activated, dendritic cells and monocytes. These immune cells may be considered as differential cells of the two diseases.
There are still many limitations in this experiment. In the future, we will collect more synovial samples from RA and SpA patients, detect the differences of various immune cells by flow cytometry, and obtain more accurate diagnostic markers.
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